Triple
T10989492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 81-740/741 Rusich |
E259718
|
entity |
| Predicate | trainsetComposition |
P96474
|
FINISHED |
| Object | 3-car trainsets |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 3-car trainsets | Statement: [81-740/741 Rusich, trainsetComposition, 3-car trainsets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainsetComposition Context triple: [81-740/741 Rusich, trainsetComposition, 3-car trainsets]
-
A.
trainingSetSize
Indicates the number of examples or instances included in a dataset used to train a model or system.
-
B.
trainingDataType
Indicates the type or category of data used for training a model, system, or process.
-
C.
trainsetType
Indicates the specific category or role of a dataset within a training process (e.g., training, validation, or test set).
-
D.
trainingPopulation
Indicates that one entity serves as the group of individuals or instances used to train or develop another entity, typically a model, system, or process.
-
E.
trainingDataIncludes
Indicates that one entity’s training dataset contains or incorporates the other entity as part of its data.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d787b6d0b48190aaf959e2609d34e5 |
completed | April 9, 2026, 11:04 a.m. |
| PD | Predicate disambiguation | batch_69d72e9055908190b438f039574aaaaf |
completed | April 9, 2026, 4:44 a.m. |
| PDg | Predicate description generation | batch_69d732242fdc8190be77d1f730a42935 |
completed | April 9, 2026, 4:59 a.m. |
Created at: April 8, 2026, 9:24 p.m.